CN115603839A - Distributed wireless interference source positioning method facing railway communication - Google Patents

Distributed wireless interference source positioning method facing railway communication Download PDF

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CN115603839A
CN115603839A CN202211589941.4A CN202211589941A CN115603839A CN 115603839 A CN115603839 A CN 115603839A CN 202211589941 A CN202211589941 A CN 202211589941A CN 115603839 A CN115603839 A CN 115603839A
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measurement data
angle measurement
probability density
position estimation
doa angle
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CN115603839B (en
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王鹏
褚忠国
文璐
陈礼云
薛东
叶安君
李惠忠
邓彬
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China Railway First Survey and Design Institute Group Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/04—Position of source determined by a plurality of spaced direction-finders
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/42—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for mass transport vehicles, e.g. buses, trains or aircraft
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention relates to a distributed wireless interference source positioning method facing railway communication. When a railway communication interference source is positioned, the problem of direction finding cross data association of multiple observation stations for positioning and tracking of multiple moving radiation sources needs to be mainly solved. The method comprises the steps of receiving interference source signals in a region to be measured, obtaining DOA angle measurement data sets of all anchor nodes, respectively taking out one DOA angle measurement data to match, forming a measurement set, and calculating to obtain position estimation; simultaneously constructing a cost function according to the probability density function from position estimation and the probability density function of clutter in the space; searching a measurement set with minimum cost under a constraint condition by using a cost function; and resolving to obtain the final position estimation according to the measurement set with the minimum cost. The method constructs angle measurement information of a plurality of targets into a multi-dimensional distribution problem, solves the two-dimensional distribution problem after relaxing constraint, finally obtains the position information of the interference source, and achieves the purposes of reducing interference and ensuring normal communication of people.

Description

Distributed wireless interference source positioning method facing railway communication
Technical Field
The invention relates to the technical field of communication, in particular to a distributed wireless interference source positioning method for railway communication.
Background
Based on the high-speed development of the internet technology and railway construction, the railway instant messaging is increasingly demanded, and the 4G and 5G networks are used as bottom layer carriers of the instant messaging and play an important role. Railway train dispatching communication generally uses a railway special mobile communication system GSM-R and a future new generation mobile communication system 5G-R, and the systems are used as important components of railway train operation command and play an important role in maintaining train operation order, improving transport capacity, reducing safety risk and the like. However, a large number of wireless communication devices near the railway cause interference to the railway communication network and the 4G and 5G networks, which not only causes trouble to the daily communication scene of the people, but also causes the electromagnetic situation along the railway to be more and more complicated to a certain extent, affects the communication situation of GSM-R, and even causes the disconnection of GSM-R in serious cases. The situations undoubtedly reduce the reliability of railway communication network transmission and the quality of communication signals, and inevitably affect the safety of railway traffic and the communication experience of people in riding.
In order to ensure that the GSM-R and the 5G-R always keep normal working states and stabilize the demands of the public on a communication network, the wireless communication equipment of a non-partner side, namely an interference source, is monitored, and the position information of the other side is acquired. The detection and tracking of the non-cooperative target are realized by passive detection technology, and the target signal parameters can be estimated by receiving the signal of the radiation source target, so that the position of the target is detected. For non-cooperative targets, the arrival angle of the signal can be measured by the passive sensor, and then the target position estimation value is obtained by calculating the sight line intersection point of the target azimuth acquired by each sensor. However, when a plurality of targets exist in the observation range, a plurality of azimuth angles of the targets appear on the field, so that a plurality of cross points are obtained, most of the cross points belong to false positions, and the problem of 'ghost points' exists. Furthermore, considering the existence of observation false alarms and false misses, the number of false locations will increase accordingly. Therefore, the first premise of the multi-observation-station multi-radiation-source direction-finding cross positioning is to determine the corresponding relation between the observed quantity and the radiation source, namely, the measurements from the same radiation source are related to the same set as much as possible. Therefore, in order to ensure normal operation of railway train dispatching communication, the problem of direction-finding cross data association of multiple observation stations for positioning and tracking multiple moving radiation sources needs to be mainly solved.
Disclosure of Invention
The invention aims to provide a railway communication-oriented distributed wireless interference source positioning method, which solves the problem of direction finding cross data association of multiple observation stations for positioning and tracking of multiple moving radiation sources, realizes matching between angle measurement and a target, and calculates to obtain position information of an interference source.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a distributed wireless interference source positioning method facing railway communication comprises the following steps:
receiving an interference source signal in a region to be measured, and obtaining a DOA angle measurement data set of each anchor node;
respectively taking out one DOA angle measurement data from the DOA angle measurement data set of each anchor node for matching to form a measurement set, and calculating to obtain position estimation;
establishing a cost function in a simultaneous manner according to a probability density function from position estimation and a probability density function of clutter in a signal propagation space;
searching a measurement set of the minimum cost under a constraint condition by using a cost function;
and resolving to obtain the final position estimation according to the measurement set with the minimum cost.
Further, receiving an interference source signal in the region to be measured, and obtaining a DOA angle measurement data set of each anchor node, including:
setting up
Figure 233700DEST_PATH_IMAGE001
Each anchor node receives an interference source signal in a region to be measured, and measures the DOA angle of each anchor node to obtain DOA angle measurement data;
first, the
Figure 681999DEST_PATH_IMAGE002
The number of DOA angle measurement data of each anchor node is
Figure 992894DEST_PATH_IMAGE003
，
Figure 919262DEST_PATH_IMAGE004
The DOA angle measurement data of each anchor node is respectively
Figure 15525DEST_PATH_IMAGE005
And constructing DOA angle measurement data sets of all anchor nodes.
Furthermore, one DOA angle measurement data is respectively taken out from the DOA angle measurement data set of each anchor node for matching, a measurement set is formed, and the position estimation is obtained by resolving, comprising the following steps:
respectively taking out DOA angle measurement data from the DOA angle measurement data set of each anchor node for matching to form a measurement set:
Figure 103567DEST_PATH_IMAGE006
the elements in the measurement set are
Figure 901759DEST_PATH_IMAGE007
Denotes the first
Figure 631817DEST_PATH_IMAGE008
A DOA angle measurement data in the DOA angle measurement data set of each anchor node:
Figure 113745DEST_PATH_IMAGE009
wherein:
Figure 638268DEST_PATH_IMAGE010
coordinate values for position estimation;
Figure 923755DEST_PATH_IMAGE011
is as follows
Figure 926347DEST_PATH_IMAGE012
Coordinate values of the anchor nodes;
Figure 731623DEST_PATH_IMAGE013
is as follows
Figure 427046DEST_PATH_IMAGE014
Noise added during measurement of each anchor node;
settling accounts
Figure 199830DEST_PATH_IMAGE015
Deriving a position estimate
Figure 740533DEST_PATH_IMAGE016
。
Further, simultaneously constructing a cost function based on the probability density function from the position estimate and the probability density function of the clutter in the signal propagation space, comprising:
taking the missing detection into account, the probability density function of a single element in the measurement set is:
Figure 915162DEST_PATH_IMAGE018
wherein:
Figure 543938DEST_PATH_IMAGE019
is as follows
Figure 538439DEST_PATH_IMAGE020
The detection probability of the sensor of each anchor node;
Figure 148412DEST_PATH_IMAGE022
measuring the probability density function of a single angle in the set under the condition of not considering the missing detection;
Figure 911969DEST_PATH_IMAGE023
in order to define the function of indication,
Figure 214774DEST_PATH_IMAGE025
，
Figure 447303DEST_PATH_IMAGE026
is shown as
Figure 860967DEST_PATH_IMAGE027
Missing detection of each anchor node;
Figure 744610DEST_PATH_IMAGE028
is the standard deviation of the noise;
from the probability density function of the individual elements in the metrology set, the probability density function from the position estimate is expressed as:
Figure 952737DEST_PATH_IMAGE029
denote the signal propagation space as
Figure 672562DEST_PATH_IMAGE030
Assuming that the clutter is uniformly distributed in the signal propagation space, and the measurement set is all from the clutter in the signal propagation space, the probability density function of the clutter in the signal propagation space is:
Figure 624338DEST_PATH_IMAGE031
wherein:
Figure 628066DEST_PATH_IMAGE032
is a clutter in the signal propagation space;
the probability density function from position estimation and the probability density function of clutter in space are combined to construct a cost function
Figure 7095DEST_PATH_IMAGE033
：
Figure 463484DEST_PATH_IMAGE034
。
Further, finding a measurement set of minimum cost under a constraint condition by using a cost function, comprising:
establishing a constraint condition:
Figure 969683DEST_PATH_IMAGE036
wherein:
Figure 296759DEST_PATH_IMAGE037
is a binary variable, defined as
Figure 112268DEST_PATH_IMAGE039
；
Under the constraint condition, obtaining a measurement set with minimum cost:
Figure 790374DEST_PATH_IMAGE040
。
further, solving to obtain a final position estimate according to the measurement set with the minimum cost, including:
based on the measurement set with the minimum cost, relaxing the constraint condition;
under the constraint condition after relaxation, introducing Lagrange multipliers to the measurement set with the minimum cost;
iteratively updating the Lagrange multiplier and iteratively updating the cost function;
and after the iteration updating is finished, outputting the final position estimation.
Further, based on the measurement set with the minimum cost, relaxing the constraint condition of the measurement set with the minimum cost includes:
and reserving the first two constraint conditions, and relaxing other constraint conditions one by one to obtain relaxed constraint conditions:
Figure 349531DEST_PATH_IMAGE041
wherein:
Figure 813005DEST_PATH_IMAGE042
is a new binary variable after relaxation.
Further, under relaxed constraint conditions, introducing a lagrangian multiplier to the minimum-cost measurement set, including:
introducing Lagrangian multipliers to minimum-cost measurement sets
Figure 268257DEST_PATH_IMAGE043
The measurement set of the minimum cost is converted into:
Figure 699238DEST_PATH_IMAGE044
wherein:
Figure 530928DEST_PATH_IMAGE045
for the new cost function after the relaxation to be,
Figure 363755DEST_PATH_IMAGE046
。
further, iteratively updating the lagrangian multiplier, iteratively updating the cost function, and outputting a final position estimate after the iterative updating is finished, wherein the method comprises the following steps:
and continuously iterating and updating the Lagrange multiplier by using a sub-gradient algorithm, calculating the relative dual gap until the relative dual gap meets the condition, ending the iteration updating, and outputting the final position estimation.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, distributed anchor nodes are deployed near a railway line, interference signals are collected and calculated, angle measurement information of a plurality of targets is constructed into a multi-dimensional distribution problem, the Lagrangian relaxation algorithm is used for carrying out relaxation constraint on the multi-dimensional distribution problem, the Hungarian algorithm is used for solving the two-dimensional distribution problem, correct correlation information is finally obtained, and therefore position information of an interference source is correctly estimated, preprocessing is carried out on the interference source, and the purposes of reducing interference and guaranteeing normal use of 4G and 5G networks and GSM-R and 5G-R communication of people are achieved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings of the embodiments can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It should be noted that like reference numerals and letters refer to like items and, thus, once an item is defined in one embodiment, it need not be further defined and explained in subsequent embodiments. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should also be noted that although a method description refers to a sequence of steps, in some cases it may be performed in a different order than here and should not be construed as a limitation on the sequence of steps.
In an actual scene, in order to realize multi-target position estimation, a more reasonable data association method is needed, ghost points are removed, and data association capability is improved. Matching of multiple anchor node Direction of arrival (DOA) estimates is a measurement-measurement associated problem, solving such problem can be converted into a multi-dimensional distribution problem, a cost function is constructed, different distribution costs are calculated, and a global minimum cost function is used to solve the problem of multi-dimensional distribution, but the SDA problem (S-dimensional allocation problem) is an NP-hard problem (NP, nondeterministic polynomial) when the problem is more than three-dimensional, and the NP problem, which means that a complex problem cannot determine whether to find an answer within polynomial time but can verify whether the answer is correct within polynomial time, is a NP-hard problem, which means a problem that is more difficult from an algorithm point of view than the NP problem, and means that all NP problems can be solved by a function of certain polynomial time, and it is difficult to directly calculate and solve the problem, and therefore, a suboptimal algorithm is used instead, the problem of multi-dimensional distribution is solved by a relaxation algorithm, and the problem of continuous relaxation is converted into a two-dimensional distribution problem by a function of two-dimensional constraint.
Referring to fig. 1, the invention provides a distributed wireless interference source positioning method facing railway communication, which can effectively solve the problem of multi-observation station direction finding cross data association of multi-motion radiation source positioning and tracking. The method comprises the following steps:
s1: and receiving an interference source signal in the area to be measured, and obtaining a DOA angle measurement data set of each anchor node. The method specifically comprises the following steps:
s101: setting up
Figure 740641DEST_PATH_IMAGE047
The method comprises the steps that each anchor node receives an interference source signal in a region to be measured, and measures the DOA angle of each anchor node to obtain DOA angle measurement data;
s102: first, the
Figure 658918DEST_PATH_IMAGE048
The number of DOA angle measurement data of each anchor node is
Figure 559878DEST_PATH_IMAGE049
，
Figure 981632DEST_PATH_IMAGE050
The DOA angle measurement data of each anchor node are respectively the number
Figure 60578DEST_PATH_IMAGE051
And constructing DOA angle measurement data sets of all anchor nodes.
S2: and respectively taking out one DOA angle measurement data from the DOA angle measurement data set of each anchor node for matching to form a measurement set, and calculating to obtain position estimation. The method specifically comprises the following steps:
s201: respectively taking out DOA angle measurement data from the DOA angle measurement data set of each anchor node for matching to form a measurement set:
Figure 466151DEST_PATH_IMAGE052
the elements in the measurement set are
Figure 170802DEST_PATH_IMAGE053
Denotes the first
Figure 712642DEST_PATH_IMAGE054
A DOA angle measurement data in the DOA angle measurement data set of each anchor node:
Figure 696910DEST_PATH_IMAGE055
wherein:
Figure 324200DEST_PATH_IMAGE056
coordinate values estimated for the position;
Figure 301383DEST_PATH_IMAGE057
is a first
Figure 697730DEST_PATH_IMAGE058
Coordinate values of each anchor node;
Figure 367745DEST_PATH_IMAGE059
is as follows
Figure 233064DEST_PATH_IMAGE060
The noise added when the anchor node measures is considered as obedienceGaussian distribution, i.e.
Figure 748359DEST_PATH_IMAGE061
，
Figure 999212DEST_PATH_IMAGE062
Is the standard deviation of the noise;
s202: settling accounts
Figure 840129DEST_PATH_IMAGE063
Deriving a position estimate
Figure 442012DEST_PATH_IMAGE064
。
S3: and simultaneously constructing a cost function according to the probability density function from the position estimation and the probability density function of the clutter in the signal propagation space. The method specifically comprises the following steps:
s301: taking the missing detection into account, the probability density function of a single element in the measurement set is:
Figure 511730DEST_PATH_IMAGE065
wherein:
Figure 882668DEST_PATH_IMAGE066
is a first
Figure 628908DEST_PATH_IMAGE067
The detection probability of the sensor of each anchor node;
Figure 452507DEST_PATH_IMAGE068
measuring the probability density function of a single angle in the set under the condition of not considering the missing detection;
Figure 840763DEST_PATH_IMAGE069
in order to define the function of indication,
Figure 551361DEST_PATH_IMAGE070
，
Figure 734081DEST_PATH_IMAGE071
denotes the first
Figure 44977DEST_PATH_IMAGE072
Missing detection of each anchor node;
Figure 971344DEST_PATH_IMAGE073
is the standard deviation of the noise;
s302: from the probability density function of a single element in the metrology set, the probability density function from the position estimate is expressed as:
Figure 67607DEST_PATH_IMAGE074
s303: denote the signal propagation space as
Figure 421228DEST_PATH_IMAGE075
Assuming that the clutter is uniformly distributed in the signal propagation space, and the measurement set is all from the clutter in the signal propagation space, the probability density function of the clutter in the signal propagation space is:
Figure 219420DEST_PATH_IMAGE076
wherein:
Figure 949479DEST_PATH_IMAGE077
is a clutter in the signal propagation space.
The positioning of the interference source is a process of data association in the direction finding positioning process, namely, a set corresponding to a real target is selected from a plurality of measurement sets, and the probability density of the measurement sets is large, so that the problem of multidimensional distribution can be realized through the construction cost of the probability density.
S304: constructing a cost function by combining a probability density function from position estimation and a probability density function of clutter in space
Figure 883937DEST_PATH_IMAGE078
：
Figure 424770DEST_PATH_IMAGE079
S4: and converting the problem into a measurement set for finding the minimum cost by using a cost function, and finding the measurement set for the minimum cost under a constraint condition. The method specifically comprises the following steps:
s401: establishing a constraint condition:
Figure 444679DEST_PATH_IMAGE080
the constraint is established according to the limitation of the positioning scene, wherein:
Figure 712849DEST_PATH_IMAGE081
is a binary variable, defined as
Figure 767393DEST_PATH_IMAGE082
。
S402: under the constraint condition, obtaining a measurement set with minimum cost:
Figure 479128DEST_PATH_IMAGE083
s5: and resolving to obtain the final position estimation according to the measurement set with the minimum cost. The method specifically comprises the following steps:
s501: and based on the measurement set with the minimum cost, relaxing the constraint condition of the measurement set by a Lagrange relaxation algorithm. The method comprises the following steps:
and reserving the first two constraint conditions, and relaxing other constraint conditions one by one to obtain relaxed constraint conditions:
Figure 720754DEST_PATH_IMAGE084
wherein:
Figure 792615DEST_PATH_IMAGE085
is a new binary variable after relaxation.
In the process of relaxation, after relaxation
Figure 967244DEST_PATH_IMAGE086
After a constraint, convert into
Figure 833569DEST_PATH_IMAGE087
The problem of dimension distribution is solved by the method,
Figure 109961DEST_PATH_IMAGE088
when it comes to
Figure 719934DEST_PATH_IMAGE089
The multi-dimensional allocation problem is relaxed to a two-dimensional allocation problem.
S502: under relaxed constraints, lagrangian multipliers are introduced to the least costly metrology set. The method comprises the following steps:
introducing Lagrange multipliers to minimum-cost measurement sets
Figure 749070DEST_PATH_IMAGE090
And converting the measurement set with the minimum cost into:
Figure 520717DEST_PATH_IMAGE091
wherein:
Figure 18825DEST_PATH_IMAGE092
for the new cost function after the relaxation,
Figure 166910DEST_PATH_IMAGE093
the optimal feasible solution cost of the two-dimensional distribution problem is expressed as
Figure 50552DEST_PATH_IMAGE094
The optimal feasible solution of the three-dimensional distribution problem is
Figure 258680DEST_PATH_IMAGE095
The three-dimensional distribution problem has more constraints and smaller feasible solution range, so that
Figure 227773DEST_PATH_IMAGE096
And so on:
Figure 918562DEST_PATH_IMAGE097
。
s503: and iteratively updating the Lagrange multiplier and the cost function. The method comprises the following steps:
and continuously and iteratively updating the Lagrange multiplier by using a sub-gradient algorithm, and circularly updating the cost function. To improve the quality of the feasible solution, the relative dual gap is calculated
Figure 656711DEST_PATH_IMAGE098
Until the relative dual gap satisfies the condition
Figure 35739DEST_PATH_IMAGE099
Usually takes on a value of
Figure 226549DEST_PATH_IMAGE100
. An upper limit of the number of iterations may also be set, and the iteration is stopped after the upper limit is completed.
Wherein:
Figure 982016DEST_PATH_IMAGE101
，
Figure 325403DEST_PATH_IMAGE102
is shown asThe solution of the front-most optimal dual,
Figure 140913DEST_PATH_IMAGE103
，
Figure 84598DEST_PATH_IMAGE104
representing feasible solutions
Figure 112597DEST_PATH_IMAGE105
。
S504: and after the iteration updating is finished, outputting the final position estimation.
According to the method, a plurality of observation stations are used for acquiring signal data of a plurality of targets and respectively carrying out DOA angle measurement on the targets. Each possible measurement combination is analyzed, a position estimate of the corresponding combination is calculated, and a corresponding cost function is calculated. The multi-dimensional distribution problem is constructed through a cost function, a Lagrange relaxation algorithm is adopted to introduce a Lagrange multiplier, the first two constraint conditions are reserved, other constraint conditions are relaxed one by one, and the multi-dimensional distribution problem is finally converted into a two-dimensional distribution problem. And solving the two-dimensional distribution problem by adopting a Hungarian algorithm so as to obtain correct matching information, and calculating by using the data association matching relation to obtain higher-precision position estimation.
Example (b):
setting a scene to be positioned as follows: two non-cooperative target nodes, the number of anchor nodes is M =3, and the anchor nodes are respectively fixed
Figure 825338DEST_PATH_IMAGE106
Coordinates, the target node may be at
Figure 562481DEST_PATH_IMAGE107
The anchor node collects the incoming wave signal of the target node, calculates the DOA of the incoming wave and forms a measurement set
Figure 993462DEST_PATH_IMAGE108
Calculating a corresponding position estimate according to each angle in the measurement set; for each angle measurement combinationAnd calculating a corresponding probability density function, constructing a cost function, converting the data association problem into a multi-dimensional distribution problem, reducing the dimension to a two-dimensional distribution problem, and further calculating a measurement set corresponding to the minimum cost, wherein the position estimation corresponding to the measurement set is the estimation position of the target node.
According to the method, distributed anchor nodes are required to be deployed near railway lines, angle measurement information of a plurality of targets is constructed into a multi-dimensional distribution problem through acquisition and calculation of interference signals, the angle measurement information is subjected to relaxation constraint by using a Lagrange relaxation algorithm, the two-dimensional distribution problem is solved by using a Hungary algorithm, correct correlation information is finally obtained, and therefore position information of an interference source is correctly estimated, and infinite interference source positioning is achieved.
The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For a person skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.

Claims (9)

1. The distributed wireless interference source positioning method facing railway communication is characterized by comprising the following steps:
the method comprises the following steps:
receiving interference source signals in a region to be measured, and obtaining DOA angle measurement data sets of all anchor nodes;
respectively taking out one DOA angle measurement data from the DOA angle measurement data set of each anchor node for matching to form a measurement set, and calculating to obtain position estimation;
simultaneously constructing a cost function according to the probability density function from position estimation and the probability density function of clutter in a signal propagation space;
searching a measurement set with minimum cost under a constraint condition by using a cost function;
and resolving to obtain the final position estimation according to the measurement set with the minimum cost.
2. The method of claim 1, wherein:
receiving an interference source signal in a region to be measured, and obtaining a DOA angle measurement data set of each anchor node, wherein the DOA angle measurement data set comprises the following steps:
setting up
Figure 389948DEST_PATH_IMAGE001
The method comprises the steps that each anchor node receives an interference source signal in a region to be measured, and measures the DOA angle of each anchor node to obtain DOA angle measurement data;
first, the
Figure 511488DEST_PATH_IMAGE002
The number of DOA angle measurement data of each anchor node is
Figure 412448DEST_PATH_IMAGE003
，
Figure 850513DEST_PATH_IMAGE004
The DOA angle measurement data of each anchor node is respectively
Figure 178727DEST_PATH_IMAGE005
And constructing DOA angle measurement data sets of all anchor nodes.
3. The method of claim 2, wherein:
respectively taking out DOA angle measurement data from the DOA angle measurement data set of each anchor node for matching to form a measurement set, and calculating to obtain position estimation, wherein the method comprises the following steps:
respectively taking out DOA angle measurement data from the DOA angle measurement data set of each anchor node for matching to form a measurement set:
Figure 318721DEST_PATH_IMAGE006
the elements in the measurement set are
Figure 226634DEST_PATH_IMAGE007
Denotes the first
Figure 34053DEST_PATH_IMAGE008
One DOA angle measurement data in the DOA angle measurement data set of each anchor node:
Figure 283900DEST_PATH_IMAGE009
wherein:
Figure 114453DEST_PATH_IMAGE010
coordinate values for position estimation;
Figure 357215DEST_PATH_IMAGE011
is as follows
Figure 19141DEST_PATH_IMAGE012
Coordinate values of the anchor nodes;
Figure 689156DEST_PATH_IMAGE013
is as follows
Figure 554475DEST_PATH_IMAGE014
Noise added during measurement of each anchor node;
settling accounts
Figure 538612DEST_PATH_IMAGE015
To derive a position estimate
Figure 55044DEST_PATH_IMAGE016
。
4. The method of claim 3, wherein:
simultaneously constructing a cost function according to the probability density function from the position estimation and the probability density function of the clutter in the signal propagation space, wherein the cost function comprises the following steps:
taking the miss-detection into account, the probability density function of a single element in the metrology set is:
Figure 161540DEST_PATH_IMAGE017
wherein:
Figure 763423DEST_PATH_IMAGE018
is a first
Figure 98720DEST_PATH_IMAGE019
The detection probability of the sensor of each anchor node;
Figure 469659DEST_PATH_IMAGE020
measuring the probability density function of a single angle in the set under the condition of not considering the missing detection;
Figure 481477DEST_PATH_IMAGE021
in order to define the function of indication,
Figure 305077DEST_PATH_IMAGE022
，
Figure 896595DEST_PATH_IMAGE023
is shown as
Figure 872772DEST_PATH_IMAGE024
Missing detection of each anchor node;
Figure 321071DEST_PATH_IMAGE025
is the standard deviation of the noise;
from the probability density function of the individual elements in the metrology set, the probability density function from the position estimate is expressed as:
Figure 897546DEST_PATH_IMAGE026
denote the signal propagation space as
Figure 823914DEST_PATH_IMAGE027
Assuming that the clutter is uniformly distributed in the signal propagation space, and all the measurement sets are from the clutter in the signal propagation space, the probability density function of the clutter in the signal propagation space is:
Figure 107128DEST_PATH_IMAGE028
wherein:
Figure 477060DEST_PATH_IMAGE029
is a clutter in the signal propagation space;
constructing a cost function by combining a probability density function from position estimation and a probability density function of clutter in space
Figure 540831DEST_PATH_IMAGE030
：
Figure 270890DEST_PATH_IMAGE031
。
5. The method of claim 4, wherein:
finding a measurement set of minimum cost under constraint conditions by using a cost function, wherein the measurement set of minimum cost comprises the following steps:
establishing a constraint condition:
Figure 205348DEST_PATH_IMAGE032
wherein:
Figure 198712DEST_PATH_IMAGE033
is a binary variable, defined as
Figure 234932DEST_PATH_IMAGE035
；
Under constraint conditions, a minimum cost measurement set is obtained:
Figure 768681DEST_PATH_IMAGE037
。
6. the method of claim 5, wherein:
according to the measurement set with the minimum cost, calculating to obtain a final position estimation, wherein the method comprises the following steps:
based on the measurement set with the minimum cost, relaxing the constraint condition;
introducing a Lagrange multiplier to the measurement set with the minimum cost under the relaxed constraint condition;
iteratively updating the Lagrange multiplier and iteratively updating the cost function;
and after the iteration updating is finished, outputting the final position estimation.
7. The method of claim 6, wherein:
based on the measurement set with the minimum cost, relaxing the constraint condition of the measurement set with the minimum cost, comprising the following steps:
and reserving the first two constraint conditions, and relaxing other constraint conditions one by one to obtain relaxed constraint conditions:
Figure 823225DEST_PATH_IMAGE038
wherein:
Figure 987490DEST_PATH_IMAGE039
is a new binary variable after relaxation.
8. The method of claim 7, wherein:
under the relaxed constraint condition, introducing a Lagrange multiplier to the measurement set with the minimum cost, wherein the Lagrange multiplier comprises the following steps:
introducing Lagrange multipliers to minimum-cost measurement sets
Figure 494695DEST_PATH_IMAGE040
The measurement set of the minimum cost is converted into:
Figure 571149DEST_PATH_IMAGE041
wherein:
Figure 745778DEST_PATH_IMAGE042
for the new cost function after the relaxation to be,
Figure 815365DEST_PATH_IMAGE043
。
9. the method of claim 8, wherein:
iteratively updating the Lagrange multiplier and the cost function, and outputting the final position estimation after the iterative updating is finished, wherein the iterative updating comprises the following steps:
and continuously iterating and updating the Lagrange multiplier by using a sub-gradient algorithm, calculating the relative dual gap until the relative dual gap meets the condition, ending the iteration updating, and outputting the final position estimation.
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